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<p>This study presents a novel ensemble machine learning framework for anomaly detection in Internet of Things (IoT) sensor networks. Unlike conventional single-model approaches that struggle with heterogeneous and dynamic data, the proposed framework integrates Random Forest, XGBoost, and Support Vector Machine classifiers through optimized ensemble fusion to enhance detection accuracy and robustness. The method efficiently distinguishes between normal and anomalous sensor patterns arising from faults, environmental variations, or malicious activity. Experimental evaluations demonstrate that the ensemble model consistently outperforms individual classifiers across key performance indicators, offering higher precision, reduced false alarms, and improved adaptability. The proposed framework thus provides a scalable and energy-efficient solution for securing IoT environments while maintaining reliable operation under dynamic conditions.</p>

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Design and analysis of novel efficient ensemble machine learning approach for anomaly detection in IoT sensor networks

  • 期刊论文

  • 发表日期：

    2025年12月8日

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  • Rajesh Rajaan,
  • Baldev Singh,
  • Nilam Choudhary

摘要

This study presents a novel ensemble machine learning framework for anomaly detection in Internet of Things (IoT) sensor networks. Unlike conventional single-model approaches that struggle with heterogeneous and dynamic data, the proposed framework integrates Random Forest, XGBoost, and Support Vector Machine classifiers through optimized ensemble fusion to enhance detection accuracy and robustness. The method efficiently distinguishes between normal and anomalous sensor patterns arising from faults, environmental variations, or malicious activity. Experimental evaluations demonstrate that the ensemble model consistently outperforms individual classifiers across key performance indicators, offering higher precision, reduced false alarms, and improved adaptability. The proposed framework thus provides a scalable and energy-efficient solution for securing IoT environments while maintaining reliable operation under dynamic conditions.